How to Use the USAJOBS (OPM) MCP in AutoGen

Q: How do I run a USAJOBS (OPM) query using AutoGen?

You provide the tools, like searchjobs, to your AssistantAgent. Then, you initiate a conversation where multiple specialized agents debate the best strategy for analyzing the job data.

Achieve consensus on USAJOBS (OPM) data findings using AutoGen's debate framework.

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Connect USAJOBS (OPM) MCP to AutoGen

Create your Vinkius account to connect USAJOBS (OPM) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup USAJOBS (OPM) with AutoGen

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Debating Job Search Results with the MCP Server

The `search_jobs` tool provides a raw list of open positions. You can set up multiple agents—one performance agent and one risk agent—to debate which job category is most promising. The MCP Server feeds the data, and AutoGen forces the negotiation. The final decision isn't just the first result; it’s the consensus reached after competing perspectives challenge each other using the live USAJOBS (OPM) data.

Analyzing Historic Data via AutoGen

Need to know why certain historical roles vanished? Use `get_historic_joas` to pull bulk records, then let your agents debate the trends. One agent might argue that a decline in postings is normal, while another argues it signals a market shift. The MCP Server provides the raw evidence; AutoGen runs the deliberation process on that data.

Validating Criteria with Multiple Agents

Before making a recommendation, agents must validate criteria. The `get_code_list` tool supplies the valid codes for filtering. An agent dedicated to accuracy can use this list to challenge any search parameter proposed by another agent. This multi-agent setup ensures that every piece of data used in your decision-making process has been rigorously checked against the official code structure.

Setup guide

Set up USAJOBS (OPM) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes USAJOBS (OPM) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="USAJOBS (OPM)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent USAJOBS (OPM) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="USAJOBS (OPM)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent USAJOBS (OPM) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by USAJOBS. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about USAJOBS (OPM) MCP in AutoGen

You provide the tools, like `search_jobs`, to your AssistantAgent. Then, you initiate a conversation where multiple specialized agents debate the best strategy for analyzing the job data.

Yes. It's designed for non-obvious answers. If finding the 'best' role requires comparing multiple criteria, having agents debate that comparison is its core function.

The server provides announcement text, bulk historic records, and code lists. These three types of structured data fuel the debate between your AutoGen agents.

The benefit is consensus-driven decision making. You get a reasoned conclusion based on competing viewpoints, rather than just a single API call result.

The server touches job announcement text, which is the primary data type. Since this involves publicly posted employment records, all output remains within the scope of public federal information.

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